MRI-based deep-learning radiomics nomogram for differentiating FIGO 2023 stage I from stage II–IV endometrial cancer: a multicenter study

OBJECTIVES: Preoperative identification of FIGO 2023 stage I endometrial cancer (EC) is important for treatment planning. We aimed to develop and validate a deep-learning radiomics nomogram (DLRN) based on multiparametric MRI to distinguish stage I EC from stage II-IV disease. MATERIALS AND METHODS: This study retrospectively enrolled patients from three centers. Patients from Centers 1 and 2 were randomly split into a training set (n = 616), and an internal test set (n = 158), and Center 3 served as an external test set (n = 232). The DLRN integrated deep-learning features, intratumoral and peritumoral radiomics features, and clinical characteristics. The DLRN was compared with clinical, radiomics, and deep-learning models. Discrimination was evaluated using the area under the receiver operating characteristic curve (AUC), with AUCs compared using DeLong's test. RESULTS: A total of 1006 patients (mean age, 56.5 ± 9.7 years) were analyzed. In the internal test set, the DLRN achieved an AUC of 0.852, higher than the clinical (0.673; p = 0.001), original radiomics (0.765; p = 0.030), and combined radiomics (0.793; p = 0.050) models. In the external test set, the DLRN remained robust (AUC = 0.829) and outperformed the clinical, original radiomics, and combined radiomics models (AUC = 0.680, 0.681, and 0.743; all p < 0.005). With DLRN assistance, radiologists showed improved diagnostic performance. CONCLUSION: The MRI-based DLRN showed robust discrimination between FIGO 2023 stage I and stage II-IV EC across test sets, supporting its potential for preoperative risk stratification and decision support. KEY POINTS: Question The introduction of the FIGO 2023 staging system has made the differentiation between stages more challenging, highlighting the need for better tools. Findings A new MRI-based deep-learning radiomics nomogram (DLRN) accurately identifies high-risk patients, significantly outperforming traditional and individual clinical prediction methods. Critical relevance statement This tool helps doctors make better preoperative treatment plans, potentially improving patient outcomes in endometrial cancer.